CPS: Synergy: Learning to Walk - Optimal Gait Synthesis and Online Learning for Terrain-Aware Legged Locomotion

CPS：协同：学习行走 - 地形感知腿部运动的最佳步态合成和在线学习

• 批准号: 1544857
• 负责人: Patricio Vela
• 金额: $ 80万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544857&HistoricalAwards=false
• 关键词: CPS; Synergy; Learning; Walk; Optimal

Legged robots have captured the imagination of society at large, throughentertainment and through the dissemination of research findings. Yet,today's reality of what (bipedal) legged robots can do falls short ofsociety's vision. A big part of the reason is that legged robots areviewed as surrogates for humans, able to go wherever humans can as aidsor as assistants where it might also be too dangerous or risky. It isin the expectation of robustness and walking facility that today'sresearch hits its limits, especially when the terrain has granularproperties. Impeding progress is the lack of a holistic approach to thecyber-physical modeling and control of legged robots. The vision ofthis work is to unite experts in granular mechanics, optimal control,and learning theory in order to define a methodology for advancingcyber-physical systems (CPS) involving a tight coupling of the physical withthe cyber through dynamic interactions that must be learned online. Theproposed work will advance the science of cyber-physical systems by moreexplicitly tying sensing, perception, and computing to the optimizationand control of physical systems whose properties are variable anduncertain. Achieving reliable, adaptive legged locomotion over terrainwith arbitrary granular properties would transform several applicationdomain areas of robotics; e.g., disaster response, agricultural andindustrial robotics, and planetary robotics. More broadly, the sametools would apply to related CPS with regards to terrain awareexoskeleton and rehabilitation prosthetics for persons with missing,non-functional, or injured legs, as well as to energy networks withtime-varying, nonlinear dynamics models.The CPS platform to be studied is that of a bipedal robot locomotingover granular ground material with uncertain physical properties (sand,gravel, dirt, etc.). The proposed work seeks to overcome currentimpediments to reliable legged locomotion over uncertain terrain type,which fundamentally relies on the controlled interaction of the robot'sfeet with the physical environment. The research goal is to improve theperception and control of legged locomotion over granular media for theexpress purpose of achieving robust, adaptive, terrain-aware locomotion.It revolves around the hypothesis that simple models with decentpredictive performance and low computational overhead are sufficient forthe optimal control formulations as the compute-constrained adaptivesubsystem will both learn and classify the peculiarities of the terrainonline. The main research objectives will involve: [1] a validatedco-simulation platform for legged robot movement over granular media;[2] terrain-dependent, stable gait generation and gait transitionstrategies via optimal control; [3] online, compute-constrained learningof granular interactions for adaptation and terrain classification; and[4] validated contributions using experimental testbeds involvingvariable and unknown (to the robot) granular media. Given the highvalue of the robotic platforms and the research with regards to outreachand participation, they will be used as outreach tools and to create neweducational modules for promotion of STEM fields. Further, themulti-disciplinary nature of the work will be highlighted in order toemphasize its importance.

通过娱乐和传播研究成果，有腿机器人已经抓住了整个社会的想象力。 然而，今天的现实是什么（两足）腿机器人可以做福尔斯短的社会愿景。 很大一部分原因是腿式机器人被视为人类的替代品，能够去任何人类可以帮助的地方，或者在可能太危险或风险太大的地方充当助手。 正是在对鲁棒性和步行便利性的期望中，今天的研究达到了极限，特别是当地形具有颗粒特性时。 阻碍进步的是缺乏一个整体的方法来对腿式机器人的网络物理建模和控制。 这项工作的愿景是团结专家在颗粒力学，最优控制和学习理论，以确定一种方法，推进网络物理系统（CPS）涉及的物理与网络通过动态的互动，必须在线学习的紧密耦合。 拟议的工作将通过更明确地将传感，感知和计算与物理系统的优化和控制联系起来，从而推进网络物理系统的科学，这些物理系统的属性是可变的和不确定的。 在具有任意颗粒特性的地形上实现可靠的、自适应的腿部运动将改变机器人的几个应用领域;例如，灾难响应，农业和工业机器人，以及行星机器人。 更广泛地说，同样的工具将适用于相关的CPS关于地形感知外骨骼和康复假肢的人失踪，无功能，或受伤的腿，以及能源网络与时变，非线性动力学models.The CPS平台被研究的是，一个双足机器人pronotingover颗粒地面材料与不确定的物理特性（沙子，砾石，污垢等）。 所提出的工作旨在克服目前的障碍，以可靠的腿运动在不确定的地形类型，这从根本上依赖于受控的相互作用的机器人'sfeet与物理环境。 研究目标是改善颗粒介质上的腿运动的感知和控制，以实现鲁棒的、自适应的、地形感知的运动，它围绕着一个假设，即具有良好预测性能和低计算开销的简单模型足以用于最优控制公式，因为计算约束的自适应子系统将在线学习和分类地形的特性。 主要研究目标将包括：[1]一个有效的联合仿真平台，用于腿式机器人在颗粒介质上的运动;[2]通过最优控制实现与地形相关的稳定步态生成和步态转换策略; [3]在线，计算约束的颗粒相互作用学习，用于适应和地形分类;以及[4]使用实验测试床进行有效的贡献，涉及可变和未知（机器人）颗粒介质。 鉴于机器人平台的高价值以及关于外联和参与的研究，它们将被用作外联工具，并创建新的教育模块，以促进STEM领域。 此外，将突出工作的多学科性质，以强调其重要性。

项目成果

Good Line Cutting: Towards Accurate Pose Tracking of Line-Assisted VO/VSLAM

• DOI: 10.1007/978-3-030-01216-8_32
• 发表时间: 2018-09
• 作者: Yipu Zhao;P. Vela

Dynamic Walking: Toward Agile and Efficient Bipedal Robots

• DOI: 10.1146/annurev-control-071020-045021
• 发表时间: 2021-01-01
• 期刊: ANNUAL REVIEW OF CONTROL, ROBOTICS, AND AUTONOMOUS SYSTEMS, VOL 4, 2021
• 作者: Reher, Jenna;Ames, Aaron D.
• 通讯作者: Ames, Aaron D.